Abstract:

Inverse parameter estimation of process-based
models is a long-standing problem in many scientific disciplines.
A key question for inverse parameter estimation
is how to define the metric that quantifies how well model
predictions fit to the data. This metric can be expressed by
general cost or objective functions, but statistical inversion
methods require a particular metric, the probability of observing
the data given the model parameters, known as the
likelihood.
For technical and computational reasons, likelihoods for
process-based stochastic models are usually based on general
assumptions about variability in the observed data, and not
on the stochasticity generated by the model. Only in recent
years have new methods become available that allow the generation
of likelihoods directly from stochastic simulations.
Previous applications of these approximate Bayesian methods
have concentrated on relatively simple models. Here, we
report on the application of a simulation-based likelihood
approximation for FORMIND, a parameter-rich individualbased
model of tropical forest dynamics.
We show that approximate Bayesian inference, based on
a parametric likelihood approximation placed in a conventional
Markov chain Monte Carlo (MCMC) sampler, performs
well in retrieving known parameter values from virtual
inventory data generated by the forest model. We analyze
the results of the parameter estimation, examine its sensitivity
to the choice and aggregation of model outputs and
observed data (summary statistics), and demonstrate the application
of this method by fitting the FORMIND model to
field data from an Ecuadorian tropical forest. Finally, we discuss
how this approach differs from approximate Bayesian
computation (ABC), another method commonly used to generate
simulation-based likelihood approximations.
Our results demonstrate that simulation-based inference,
which offers considerable conceptual advantages over more
traditional methods for inverse parameter estimation, can be
successfully applied to process-based models of high complexity.
The methodology is particularly suitable for heterogeneous
and complex data structures and can easily be adjusted
to other model types, including most stochastic population
and individual-based models. Our study therefore provides
a blueprint for a fairly general approach to parameter
estimation of stochastic process-based models.

Abstract:

Shallow landslides are an important type of natural ecosystem disturbance in tropical montane forests. Due to landslides, vegetation and often also the upper soil layer are removed, and space for primary succession under altered environmental conditions is created. Little is known about how these altered conditions affect important aspects of forest recovery such as the establishment of new tree biomass and species composition. To address these questions we utilize a process-based forest simulation model and develop potential forest regrowth scenarios. We investigate how changes in different trees species characteristics influence forest recovery on landslide sites. The applied regrowth scenarios are: undisturbed regrowth (all tree species characteristics remain like in the undisturbed forest), reduced tree growth (induced by nutrient limitation), reduced tree establishment (due to thicket-forming vegetation and dispersal limitation) and increased tree mortality (due to post-landslide erosion and increased susceptibility). We then apply these scenarios to an evergreen tropical montane forest in southern Ecuador where landslides constitute a major source of natural disturbance. Our most important findings are
(a)
On the local scale of a single landslide tree biomass recovers within the first 80 years after landslides for most scenarios, but it takes at least 200 years for the post-landslide forest to reach a structure (in terms of stem size distribution) similar to a mature forest. On this scale forest productivity is reduced for most regrowth scenarios. Changes in different tree species characteristics produce distinct spatio-temporal patterns of tree biomass distribution in the first decades of recovery within the landslide disturbed area. These patterns can potentially be used for identifying the dominant processes that drive forest recovery on landslide disturbed sites.
(b)
On the larger scale of the landscape overall tree biomass is reduced by 9?15% due to landslide disturbances. Overall forest productivity is only slightly reduced (<6%), but landslides increase landscape heterogeneity and produce hotspots of biomass loss and ?blind spots? of forest productivity. Thus landslides have a strong impact on the distribution of biomass in tropical montane forests.
This study demonstrates that dynamic forest models are useful tools for complementing field based studies on landslides; they allow for testing alternative hypotheses on different sources of heterogeneity across spatial scales and investigating the influence of landslides on long-term forest dynamics.

Abstract:

Background:
Tropical forests play an important role in the global carbon (C) cycle. However, tropical montane forests have been studied less than tropical lowland forests, and their role in carbon storage is not well understood. Montane forests are highly endangered due to logging, land-use and climate change. Our objective was to analyse how the carbon balance changes during forest succession.
Methods:
In this study, we used a method to estimate local carbon balances that combined forest inventory data with process-based forest models. We utilised such a forest model to study the carbon balance of a tropical montane forest in South Ecuador, comparing two topographical slope positions (ravines and lower slopes vs upper slopes and ridges).
Results: The simulation results showed that the forest acts as a carbon sink with a maximum net ecosystem exchange (NEE) of 9.3 Mg C?(ha?yr)?1 during its early successional stage (0–100 years). In the late successional stage, the simulated NEE fluctuated around zero and had a variation of 0.77 Mg C?(ha?yr) –1. The simulated variability of the NEE was within the range of the field data. We discovered several forest attributes (e.g., basal area or the relative amount of pioneer trees) that can serve as predictors for NEE for young forest stands (0–100 years) but not for those in the late
successional stage (500–1,000 years). In case of young forest stands these correlations are high, especially between stand basal area and NEE.
Conclusion:
In this study, we used an Ecuadorian study site as an example of how to successfully link a forest model with forest inventory data, for estimating stem-diameter distributions, biomass and aboveground net primary productivity. To conclude, this study shows that process-based forest models can be used to investigate the carbon balance of tropical montane forests. With this model it is possible to find hidden relationships between forest attributes and forest carbon fluxes. These relationships promote a better understanding of the role of tropical montane forests in
the context of global carbon cycle, which in future will become more relevant to a society under global change.